Research
The Innovative AI Platform for Enhancing Concrete Sustainability and Climate Resilience (AI-Sustainability-CO₂ Capture) project addresses the urgent need to reduce the CO₂ footprint of the construction sector while safeguarding the sustainable development in construction under changing climate conditions. Concrete can uptake significant amounts of CO₂ via carbonation, but this process also leads to accelerated steel reinforcement corrosion, causing critical sustainability and safety issues. EU studies estimate that repair costs linked to carbonation-induced deterioration may reach hundreds of billions of euros by 2050, making this a major scientific, societal, and economic challenge.
To address this issue, the AI-Sustainability-CO₂ Capture project proposes an AI-driven optimisation platform that integrates experimental campaigns, mesoscale modelling, and dynamic climate scenario simulations to balance carbon sequestration and sustainable development in construction. The project studies how concrete materials behave under different environmental conditions, including future climate scenarios such as rising temperatures and humidity changes. By integrating physical models of material behaviour with machine learning techniques, the platform can predict how concrete will evolve over time and how much CO₂ it can safely store without compromising durability.
Based on these predictions, AI-based optimisation tools will be used to design improved concrete mixtures that maximise CO₂ sequestration while extending the service life of infrastructure. The final outcome is an open and user-friendly digital platform that allows engineers, designers, and decision-makers to evaluate sustainable material solutions under realistic conditions. Overall, the AI-Sustainability-CO₂ Capture project will contribute to climate change mitigation, more sustainable cities, and more resilient infrastructures across Europe and beyond.
Biography
I am currently a postdoctoral researcher at Université Paris-Saclay, France, working on the MINERVE project, where I develop computational models and machine learning-based approaches to assess the vulnerability of railway infrastructure exposed to natural hazards. My research integrates physics-based simulations with data-driven methods to support infrastructure risk assessment and hazard mapping under complex environmental conditions. Previously, I worked as a postdoctoral researcher at Université libre de Bruxelles, Belgium, focusing on the development of sustainable construction materials, with particular emphasis on the valorisation of industrial and waste-derived sulfur in cementitious systems. I obtained my PhD in Civil Engineering from Aarhus University, Denmark, where I investigated the durability and performance of cement-based materials, including the use of supplementary cementitious materials and recycled industrial by-products to enhance sustainability. My research background combines computational modelling, experimental materials science, and data-driven engineering approaches. This interdisciplinary expertise directly supports the objectives of the IRIS Postdoctoral COFUND project, particularly in evaluating material performance, durability, and sustainability.